Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S.

Affiliation

Park J; Korea Advanced Inst. of Science and Technology, Dept. of Industrial and Systems Engineering, Daejeon 34141, Republic of Korea.
Lechevalier D; Université de Bourgogne, Laboratoire d'Electronique, Informatique et Image, Dijon 21000, France.
Ak R; National Inst. of Standards and Technology, Engineering Lab, Gaithersburg, MD 20899.
Ferguson M; Stanford Univ., Dept. of Civil and Environmental Engineering, Stanford, CA 94305-4020.
Law KH; Stanford Univ., Dept. of Civil and Environmental Engineering, Stanford, CA 94305-4020.
Lee YT; National Inst. of Standards and Technology, Engineering Lab, Gaithersburg, MD 20899.
Rachuri S; Dept. of Energy, Advanced Manufacturing Office, Washington, DC 20585.

Smart Sustain Manuf Syst ; 1(1): 121-141, 2017.

Article de En | MEDLINE | ID: mdl-29202125

Mots clés

Gaussian process regression; XML; data mining; predictive analytics; predictive model markup language (PMML); standards

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Smart Sustain Manuf Syst Année: 2017 Type de document: Article

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